Upload LFM2.5-230M

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LICENSE

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README.md

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+---
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+- ar
+- zh
+- fr
+- de
+- ja
+- ko
+- es
+- pt
+- it
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2.5
+- edge
+base_model: LiquidAI/LFM2.5-230M-Base
+---
+
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
+
+# LFM2.5-230M
+
+LFM2.5 is a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.
+
+- **Our most compact model yet**: 230M parameters that punch above their weight, bringing real capability to the tightest memory and compute budgets.
+- **Fast edge inference**: Best throughput from low-cost CPUs to production GPUs, running at 213 tok/s decode speed on Galaxy S25 Ultra and 42 tok/s on a Raspberry Pi 5.
+- **Built for agentic tasks**: Distilled from LFM2.5-350M and refined with multi-stage reinforcement learning, making it well-suited for tool use and data extraction.
+
+Find more information about LFM2.5-230M in our [blog post](https://www.liquid.ai/blog/lfm2-5-230m).
+
+![lfm2_5_230m_benchmarks](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/4UpNxlgfKjfgT5ByIVph0.png)
+
+## 🗒️ Model Details
+
+| Model | Parameters | Description |
+|-------|------------|-------------|
+| [LFM2.5-230M-Base](https://huggingface.co/LiquidAI/LFM2.5-230M-Base) | 230M | Pre-trained base model for fine-tuning |
+| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | 230M | General-purpose instruction-tuned model |
+
+LFM2.5-230M is a general-purpose text-only model with the following features:
+
+- **Number of parameters**: 230M
+- **Number of layers**: 14 (8 double-gated LIV convolution blocks + 6 GQA blocks)
+- **Training budget**: 19T tokens
+- **Context length**: 32,768 tokens
+- **Vocabulary size**: 65,536
+- **Knowledge cutoff**: Mid-2024
+- **Languages**: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish
+- **Generation parameters**:
+  - `temperature: 0.1`
+  - `top_k: 50`
+  - `repetition_penalty: 1.05`
+
+| Model | Description |
+|-------|-------------|
+| [**LFM2.5-230M**](https://huggingface.co/LiquidAI/LFM2.5-230M) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. |
+| [LFM2.5-230M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-230M-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. |
+| [LFM2.5-230M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-230M-ONNX) | ONNX Runtime format for cross-platform deployment. |
+| [LFM2.5-230M-MLX](https://huggingface.co/LiquidAI/LFM2.5-230M-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. |
+
+We recommend using it for data extraction and lightweight on-device agentic pipelines. It is not recommended for reasoning-heavy workloads such as advanced math, code generation, or creative writing.
+
+### Chat Template
+
+LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) for details. Example:
+
+```
+<|startoftext|><|im_start|>system
+You are a helpful assistant trained by Liquid AI.<|im_end|>
+<|im_start|>user
+What is C. elegans?<|im_end|>
+<|im_start|>assistant
+```
+
+You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically.
+
+### Tool Use
+
+LFM2.5 supports function calling in four steps:
+
+1. **Function definition**: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`.
+2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt.
+3. **Function execution**: Execute the call and return the result with the `tool` role.
+4. **Final answer**: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.
+
+See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide. Example:
+
+```
+<|startoftext|><|im_start|>system
+List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
+<|im_start|>user
+What is the current status of candidate ID 12345?<|im_end|>
+<|im_start|>assistant
+<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
+<|im_start|>tool
+[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
+<|im_start|>assistant
+The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>
+```
+
+## 🏃 Inference
+
+LFM2.5 is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list.
+
+| Name | Description | Docs | Notebook |
+|------|-------------|------|:--------:|
+| [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — |
+| [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — |
+| [SGLang](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang">Link</a> | - </a> |
+
+
+Quick start with Transformers (compatible with `transformers>=5.0.0`):
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model_id = "LiquidAI/LFM2.5-230M"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    dtype="bfloat16",
+#   attn_implementation="flash_attention_2" <- uncomment on compatible GPU
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+prompt = "What is C. elegans?"
+
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+)["input_ids"].to(model.device)
+
+output = model.generate(
+    input_ids,
+    do_sample=True,
+    temperature=0.1,
+    top_k=50,
+    repetition_penalty=1.05,
+    max_new_tokens=512,
+    streamer=streamer,
+)
+```
+
+## 🔧 Fine-Tuning
+
+We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results.
+
+| Name | Description | Docs | Notebook |
+|------|-------------|------|----------|
+| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+| GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> |
+
+## 📊 Performance
+
+### Benchmarks
+
+| Model | GPQA Diamond | MMLU-Pro | IFEval | IFBench | Multi-IF |
+|---|---|---|---|---|---|
+| **LFM2.5-230M** | 25.41 | 20.25 | 71.71 | 38.40 | 37.70 |
+| LFM2.5-350M | 30.64 | 20.01 | 76.96 | 40.69 | 44.92 |
+| LFM2-350M | 27.58 | 19.29 | 64.96 | 18.20 | 32.92 |
+| Granite 4.0-H-350M | 22.32 | 13.14 | 61.27 | 17.22 | 28.70 |
+| Granite 4.0-350M | 25.91 | 12.84 | 53.48 | 15.98 | 24.21 |
+| Qwen3.5-0.8B (Instruct) | 27.41 | 37.42 | 59.94 | 22.87 | 41.68 |
+| Gemma 3 1B IT | 23.89 | 14.04 | 63.49 | 20.33 | 44.25 |
+
+| Model | CaseReportBench | BFCLv3 | BFCLv4 | τ²-Bench Telecom | τ²-Bench Retail |
+|---|---|---|---|---|---|
+| **LFM2.5-230M** | 22.51 | 43.26 | 21.03 | 5.26 | 13.68 |
+| LFM2.5-350M | 32.45 | 44.11 | 21.86 | 18.86 | 17.84 |
+| LFM2-350M | 11.67 | 22.95 | 12.29 | 10.82 | 5.56 |
+| Granite 4.0-H-350M | 12.44 | 43.07 | 13.28 | 13.74 | 6.14 |
+| Granite 4.0-350M | 0.84 | 39.58 | 13.73 | 2.92 | 6.14 |
+| Qwen3.5-0.8B (Instruct) | 13.83 | 35.08 | 18.70 | 12.57 | 6.14 |
+| Gemma 3 1B IT | 2.28 | 16.61 | 7.17 | 9.36 | 6.43 |
+
+### CPU Inference
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TCR-MfPtX3YTPvRzxWcG3.png)
+
+### GPU Inference
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/emlcz4gf2wendPhKQWEBN.png)
+
+## 📬 Contact
+
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
+
+## Citation
+
+```bibtex
+@article{liquidAI2026230M,
+  author = {Liquid AI},
+  title = {LFM2.5-230M: Built to Run Anywhere},
+  journal = {Liquid AI Blog},
+  year = {2026},
+  note = {www.liquid.ai/blog/lfm2-5-230m},
+}
+```
+```bibtex
+@article{liquidai2025lfm2,
+  title={LFM2 Technical Report},
+  author={Liquid AI},
+  journal={arXiv preprint arXiv:2511.23404},
+  year={2025}
+}
+```

chat_template.jinja

@@ -0,0 +1,115 @@
+{{- bos_token -}}
+{%- set preserve_thinking = preserve_thinking | default(false) -%}
+
+{%- macro format_arg_value(arg_value) -%}
+    {%- if arg_value is string -%}
+        {{- "'" + arg_value + "'" -}}
+    {%- elif arg_value is mapping -%}
+        {{- arg_value | tojson -}}
+    {%- else -%}
+        {{- arg_value | string -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro parse_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- else -%}
+        {%- set _ns = namespace(result="") -%}
+        {%- for item in content -%}
+            {%- if item["type"] == "image" -%}
+                {%- set _ns.result = _ns.result + "<image>" -%}
+            {%- elif item["type"] == "text" -%}
+                {%- set _ns.result = _ns.result + item["text"] -%}
+            {%- else -%}
+                {%- set _ns.result = _ns.result + item | tojson -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {{- _ns.result -}}
+    {%- endif -%}
+{%- endmacro -%}
+
+{%- macro render_tool_calls(tool_calls) -%}
+    {%- set tool_calls_ns = namespace(tool_calls=[]) -%}
+    {%- for tool_call in tool_calls -%}
+        {%- set func_name = tool_call["function"]["name"] -%}
+        {%- set func_args = tool_call["function"]["arguments"] -%}
+        {%- set args_ns = namespace(arg_strings=[]) -%}
+        {%- for arg_name, arg_value in func_args.items() -%}
+            {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
+        {%- endfor -%}
+        {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
+    {%- endfor -%}
+    {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
+{%- endmacro -%}
+
+{%- set ns = namespace(system_prompt="", last_user_index=-1) -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- if messages[0].get("content") -%}
+        {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%}
+    {%- endif -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "user" -%}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message.role + "\n" -}}
+    {%- if message.role == "assistant" -%}
+        {%- generation -%}
+        {%- if message.thinking is defined and (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+            {{- "<think>" + message.thinking + "</think>" -}}
+        {%- endif -%}
+        {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%}
+        {%- set _has_cfm = false -%}
+        {%- if message.content is defined -%}
+            {%- set content = parse_content(message.content) -%}
+            {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%}
+                {%- if "</think>" in content -%}
+                    {%- set content = content.split("</think>")[-1] | trim -%}
+                {%- endif -%}
+            {%- endif -%}
+            {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%}
+                {%- set _has_cfm = true -%}
+                {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%}
+                {{- content[:_trunc_len] -}}
+            {%- else -%}
+                {{- content -}}
+            {%- endif -%}
+        {%- endif -%}
+        {%- if message.tool_calls is defined -%}
+            {{- render_tool_calls(message.tool_calls) -}}
+        {%- endif -%}
+        {%- if _has_cfm -%}
+            {{- _cfm_tag -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+        {%- endgeneration -%}
+    {%- else %}
+        {%- if message.get("content") -%}
+            {{- parse_content(message["content"]) -}}
+        {%- endif -%}
+        {{- "<|im_end|>\n" -}}
+    {%- endif %}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

@@ -0,0 +1,66 @@
+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block__name_mlp": "parallel_mlp_merged",
+  "block_auto_adjust_ff_dim": false,
+  "block_dim": 1024,
+  "block_ff_dim": 2560,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_ffn_te_autocast": false,
+  "block_ffn_use_quantized_params": false,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_sequence_parallel_norm_across_tp": false,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 1024,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "ffn_te_autocast": false,
+  "ffn_use_quantized_params": false,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 2560,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_heads": 16,
+  "num_hidden_layers": 14,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sequence_parallel_norm_across_tp": false,
+  "tie_embedding": true,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "use_cache": true,
+  "use_pos_enc": true,
+  "vocab_size": 65536
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 7,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "do_sample": true,
+  "temperature": 0.1,
+  "top_k": 50,
+  "repetition_penalty": 1.05,
+  "transformers_version": "5.2.0",
+  "use_cache": true
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f630da86651136c9aee893b04b7542007e90fdd718355358e57e7ecc31517cfd
+size 459401112

tokenizer_config.json

@@ -0,0 +1,19 @@
+{
+  "backend": "tokenizers",
+  "bos_token": "<|startoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "is_local": true,
+  "legacy": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}