Instructions to use staeiou/bartleby-qwen3-1.7b_v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use staeiou/bartleby-qwen3-1.7b_v5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="staeiou/bartleby-qwen3-1.7b_v5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("staeiou/bartleby-qwen3-1.7b_v5")
model = AutoModelForCausalLM.from_pretrained("staeiou/bartleby-qwen3-1.7b_v5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use staeiou/bartleby-qwen3-1.7b_v5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="staeiou/bartleby-qwen3-1.7b_v5",
	filename="bartleby-qwen3-1.7b_v5-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use staeiou/bartleby-qwen3-1.7b_v5 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

Use Docker

docker model run hf.co/staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

LM Studio
Jan

vLLM

How to use staeiou/bartleby-qwen3-1.7b_v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "staeiou/bartleby-qwen3-1.7b_v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "staeiou/bartleby-qwen3-1.7b_v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

SGLang

How to use staeiou/bartleby-qwen3-1.7b_v5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "staeiou/bartleby-qwen3-1.7b_v5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "staeiou/bartleby-qwen3-1.7b_v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "staeiou/bartleby-qwen3-1.7b_v5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "staeiou/bartleby-qwen3-1.7b_v5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use staeiou/bartleby-qwen3-1.7b_v5 with Ollama:
```
ollama run hf.co/staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
```

Unsloth Studio

How to use staeiou/bartleby-qwen3-1.7b_v5 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for staeiou/bartleby-qwen3-1.7b_v5 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for staeiou/bartleby-qwen3-1.7b_v5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for staeiou/bartleby-qwen3-1.7b_v5 to start chatting

Atomic Chat new
Docker Model Runner
How to use staeiou/bartleby-qwen3-1.7b_v5 with Docker Model Runner:
```
docker model run hf.co/staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M
```

Lemonade

How to use staeiou/bartleby-qwen3-1.7b_v5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull staeiou/bartleby-qwen3-1.7b_v5:Q4_K_M

Run and chat with the model

lemonade run user.bartleby-qwen3-1.7b_v5-Q4_K_M

List all available models

lemonade list

staeiou commited on Mar 26

Commit

2f6e15c

verified ·

1 Parent(s): e2ef65e

Upload folder using huggingface_hub

Browse files

Files changed (11) hide show

.gitattributes +3 -0
README.md +58 -0
bartleby-qwen3-1.7b_v5-Q4_K_M.gguf +3 -0
bartleby-qwen3-1.7b_v5-Q8_0.gguf +3 -0
chat_template.jinja +1 -0
config.json +66 -0
generation_config.json +13 -0
logs.log +31 -0
model.safetensors +3 -0
tokenizer.json +3 -0
tokenizer_config.json +15 -0

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+bartleby-qwen3-1.7b_v5-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+bartleby-qwen3-1.7b_v5-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

	@@ -0,0 +1,58 @@

+---
+base_model: unsloth/Qwen3-1.7B
+library_name: transformers
+model_name: bartleby-qwen3-1.7b_v5
+tags:
+- generated_from_trainer
+- sft
+- unsloth
+- trl
+licence: license
+---
+# Model Card for bartleby-qwen3-1.7b_v5
+This model is a fine-tuned version of [unsloth/Qwen3-1.7B](https://huggingface.co/unsloth/Qwen3-1.7B).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+## Quick start
+```python
+from transformers import pipeline
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+## Training procedure
+This model was trained with SFT.
+### Framework versions
+- TRL: 0.24.0
+- Transformers: 5.3.0
+- Pytorch: 2.9.0
+- Datasets: 4.3.0
+- Tokenizers: 0.22.2
+## Citations
+Cite TRL as:
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}

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chat_template.jinja ADDED Viewed

	@@ -0,0 +1 @@


1	+ {% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}

config.json ADDED Viewed

	@@ -0,0 +1,66 @@

+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_name": "unsloth/Qwen3-1.7B",
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151669,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.3.5",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151669,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "5.3.0"
+}

logs.log ADDED Viewed

	@@ -0,0 +1,31 @@

+================================================================================
+BARTLEBY FULL FINETUNE _ 16-BIT _ AUTO TEMPLATE+MASK DETECT _ LAST-ANSWER MULTITURN
+================================================================================
+MODEL      : unsloth/Qwen3-1.7B
+DATA       : data/training_data_v2_filtered.jsonl
+GOLD       : data/gold_seed_training_data_sosts.jsonl
+OUTPUT     : staeiou/bartleby-qwen3-1.7b_v5
+CACHE_DIR  : /workspace/.cache/huggingface/datasets
+SEQ        : 1024
+PACKING    : False
+LOAD_4BIT  : False (forced 16-bit base)
+FULL_FT    : True
+REMOTE_CODE: False
+FAMILY     : qwen
+TRL_COMPAT : ConstantLengthDataset patched=True
+ADAPTERS   : disabled
+TRAIN      : bs=2 grad_accum=16 eff_bs=32
+EPOCHS     : 2.0
+LR         : 1e-05 scheduler=cosine warmup=0.03 weight_decay=0.05 max_grad_norm=1.0
+MULTITURN  : num=0 max_turns=5 (only last assistant supervised)
+GOLD_REPEAT: 5
+GPU        : Single GPU (CUDA_VISIBLE_DEVICES=0)
+================================================================================
+[1/7] Loading base model...
+==((====))==  Unsloth 2026.3.5: Fast Qwen3 patching. Transformers: 5.3.0. vLLM: 0.1
+3.0.
+   \\   /|    NVIDIA RTX 5000 Ada Generation. Num GPUs = 1. Max memory: 31.475 GB.
+Platform: Linux.
+O^O/ \_/ \    Torch: 2.9.0+cu128. CUDA: 8.9. CUDA Toolkit: 12.8. Triton: 3.5.0
+\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.33.post1. FA2 = False]            "-____-"     Free license: http://github.com/unslothai/unsloth

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}