Instructions to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF",
	filename="cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-00001-of-00002.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF with Ollama:
```
ollama run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
```

Unsloth Studio

How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF 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 BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K
```

Lemonade

How to use BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BlouseJury/cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.cognitivecomputations_dolphin-2.9.1-llama-3-70b-Q6_K-GGUF-Q6_K

List all available models

lemonade list

BlouseJury commited on Sep 18, 2024

Commit

6df6a99

verified ·

1 Parent(s): f5f0c37

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+---
+license: llama3
+base_model: meta-llama/Meta-Llama-3-70B
+tags:
+- generated_from_trainer
+- axolotl
+model-index:
+- name: out
+  results: []
+datasets:
+- cognitivecomputations/Dolphin-2.9
+- teknium/OpenHermes-2.5
+- m-a-p/CodeFeedback-Filtered-Instruction
+- cognitivecomputations/dolphin-coder
+- cognitivecomputations/samantha-data
+- microsoft/orca-math-word-problems-200k
+- Locutusque/function-calling-chatml
+- internlm/Agent-FLAN
+---
+# Dolphin 2.9.1 Llama 3 70b 🐬
+Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
+[![Discord](https://img.shields.io/discord/1156064224225808488?logo=Discord&logoColor=%23ffffff&label=Discord&link=https%3A%2F%2Fdiscord.gg%2FtCMkMDDHwm)](https://discord.gg/cognitivecomputations)
+Discord: https://discord.gg/cognitivecomputations
+<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
+We have retrained our LLama-3-70b fine tune to address behavioral issues in the initial 2.9 dataset. Specifically, Systemchat was causing the model to be *too* reliant on the system prompt. Additionally, it had an occasional quirk that would cause the model to overly reference the system prompt. We also found generation length was at times not sufficient for any given task. We identified the culprit as Ultrachat. Accounting for these concerns, we removed systemchat and ultrachat from the dataset. It is otherwise identical to dolphin-2.9.
+Our appreciation for the sponsors of Dolphin 2.9.1:
+- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xL40S node
+- [OnDemand](https://on-demand.io/) - provided inference sponsorship
+This model is based on Llama-3-70b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
+The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
+It took 3 days on an 8x H100 provided by Crusoe Cloud
+This model was trained FFT on parameters selected by [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py), using ChatML prompt template format.
+example:
+```
+<|im_start|>system
+You are Dolphin, a helpful AI assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
+Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
+Dolphin is licensed according to Meta's Llama license.  We grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license.  Dolphin was trained on data generated from GPT4, among other models.
+## Evals
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/NnLaOrgAud-D_L2QEOHz4.png)
+## Training
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+<details><summary>See axolotl config</summary>
+axolotl version: `0.4.0`
+```yaml
+base_model: meta-llama/Meta-Llama-3-70B
+model_type: LlamaForCausalLM
+tokenizer_type: AutoTokenizer
+load_in_8bit: false
+# load_in_4bit: true
+strict: false
+datasets:
+  - path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+  - path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
+    type: sharegpt
+    conversation: chatml
+chat_template: chatml
+# adapter: qlora
+# lora_r: 128
+# lora_alpha: 16
+# lora_modules_to_save: [embed_tokens, lm_head]
+# lora_dropout: 0.05
+# lora_target_linear: true
+unfrozen_parameters:
+- ^lm_head.weight$
+- ^model.embed_tokens.weight$
+# mlp.down_proj layers
+- model.layers.40.mlp.down_proj
+- model.layers.44.mlp.down_proj
+- model.layers.45.mlp.down_proj
+- model.layers.46.mlp.down_proj
+- model.layers.43.mlp.down_proj
+- model.layers.52.mlp.down_proj
+- model.layers.47.mlp.down_proj
+- model.layers.48.mlp.down_proj
+- model.layers.39.mlp.down_proj
+- model.layers.49.mlp.down_proj
+- model.layers.38.mlp.down_proj
+- model.layers.53.mlp.down_proj
+- model.layers.41.mlp.down_proj
+- model.layers.35.mlp.down_proj
+- model.layers.51.mlp.down_proj
+- model.layers.42.mlp.down_proj
+- model.layers.37.mlp.down_proj
+- model.layers.50.mlp.down_proj
+- model.layers.60.mlp.down_proj
+- model.layers.76.mlp.down_proj
+- model.layers.54.mlp.down_proj
+- model.layers.36.mlp.down_proj
+- model.layers.57.mlp.down_proj
+- model.layers.56.mlp.down_proj
+- model.layers.55.mlp.down_proj
+- model.layers.77.mlp.down_proj
+- model.layers.59.mlp.down_proj
+- model.layers.61.mlp.down_proj
+- model.layers.58.mlp.down_proj
+- model.layers.65.mlp.down_proj
+- model.layers.75.mlp.down_proj
+- model.layers.64.mlp.down_proj
+- model.layers.62.mlp.down_proj
+- model.layers.68.mlp.down_proj
+- model.layers.19.mlp.down_proj
+- model.layers.66.mlp.down_proj
+# mlp.gate_proj layers
+- model.layers.70.mlp.gate_proj
+- model.layers.71.mlp.gate_proj
+- model.layers.67.mlp.gate_proj
+- model.layers.58.mlp.gate_proj
+- model.layers.55.mlp.gate_proj
+- model.layers.57.mlp.gate_proj
+- model.layers.56.mlp.gate_proj
+- model.layers.66.mlp.gate_proj
+- model.layers.72.mlp.gate_proj
+- model.layers.69.mlp.gate_proj
+- model.layers.52.mlp.gate_proj
+- model.layers.54.mlp.gate_proj
+- model.layers.62.mlp.gate_proj
+- model.layers.60.mlp.gate_proj
+- model.layers.74.mlp.gate_proj
+- model.layers.59.mlp.gate_proj
+- model.layers.68.mlp.gate_proj
+- model.layers.61.mlp.gate_proj
+- model.layers.73.mlp.gate_proj
+- model.layers.53.mlp.gate_proj
+- model.layers.51.mlp.gate_proj
+- model.layers.63.mlp.gate_proj
+- model.layers.48.mlp.gate_proj
+- model.layers.49.mlp.gate_proj
+- model.layers.64.mlp.gate_proj
+- model.layers.50.mlp.gate_proj
+- model.layers.65.mlp.gate_proj
+- model.layers.47.mlp.gate_proj
+- model.layers.44.mlp.gate_proj
+- model.layers.45.mlp.gate_proj
+- model.layers.75.mlp.gate_proj
+- model.layers.46.mlp.gate_proj
+- model.layers.43.mlp.gate_proj
+- model.layers.77.mlp.gate_proj
+- model.layers.41.mlp.gate_proj
+- model.layers.42.mlp.gate_proj
+# mlp.up_proj layers
+- model.layers.70.mlp.up_proj
+- model.layers.67.mlp.up_proj
+- model.layers.66.mlp.up_proj
+- model.layers.69.mlp.up_proj
+- model.layers.62.mlp.up_proj
+- model.layers.65.mlp.up_proj
+- model.layers.63.mlp.up_proj
+- model.layers.68.mlp.up_proj
+- model.layers.71.mlp.up_proj
+- model.layers.64.mlp.up_proj
+- model.layers.61.mlp.up_proj
+- model.layers.58.mlp.up_proj
+- model.layers.59.mlp.up_proj
+- model.layers.57.mlp.up_proj
+- model.layers.55.mlp.up_proj
+- model.layers.72.mlp.up_proj
+- model.layers.54.mlp.up_proj
+- model.layers.60.mlp.up_proj
+- model.layers.56.mlp.up_proj
+- model.layers.73.mlp.up_proj
+- model.layers.50.mlp.up_proj
+- model.layers.51.mlp.up_proj
+- model.layers.53.mlp.up_proj
+- model.layers.74.mlp.up_proj
+- model.layers.52.mlp.up_proj
+- model.layers.49.mlp.up_proj
+- model.layers.30.mlp.up_proj
+- model.layers.34.mlp.up_proj
+- model.layers.47.mlp.up_proj
+- model.layers.46.mlp.up_proj
+- model.layers.48.mlp.up_proj
+- model.layers.38.mlp.up_proj
+- model.layers.45.mlp.up_proj
+- model.layers.43.mlp.up_proj
+- model.layers.29.mlp.up_proj
+- model.layers.42.mlp.up_proj
+# self_attn.k_proj layers
+- model.layers.72.self_attn.k_proj
+- model.layers.75.self_attn.k_proj
+- model.layers.71.self_attn.k_proj
+- model.layers.74.self_attn.k_proj
+- model.layers.44.self_attn.k_proj
+- model.layers.31.self_attn.k_proj
+- model.layers.33.self_attn.k_proj
+- model.layers.34.self_attn.k_proj
+- model.layers.76.self_attn.k_proj
+- model.layers.78.self_attn.k_proj
+- model.layers.77.self_attn.k_proj
+- model.layers.22.self_attn.k_proj
+- model.layers.18.self_attn.k_proj
+- model.layers.60.self_attn.k_proj
+- model.layers.17.self_attn.k_proj
+- model.layers.56.self_attn.k_proj
+- model.layers.2.self_attn.k_proj
+- model.layers.21.self_attn.k_proj
+- model.layers.19.self_attn.k_proj
+- model.layers.23.self_attn.k_proj
+- model.layers.52.self_attn.k_proj
+- model.layers.35.self_attn.k_proj
+- model.layers.73.self_attn.k_proj
+- model.layers.15.self_attn.k_proj
+- model.layers.27.self_attn.k_proj
+- model.layers.29.self_attn.k_proj
+- model.layers.20.self_attn.k_proj
+- model.layers.28.self_attn.k_proj
+- model.layers.36.self_attn.k_proj
+- model.layers.25.self_attn.k_proj
+- model.layers.37.self_attn.k_proj
+- model.layers.30.self_attn.k_proj
+- model.layers.16.self_attn.k_proj
+- model.layers.32.self_attn.k_proj
+- model.layers.41.self_attn.k_proj
+- model.layers.26.self_attn.k_proj
+# self_attn.o_proj layers
+- model.layers.50.self_attn.o_proj
+- model.layers.61.self_attn.o_proj
+- model.layers.46.self_attn.o_proj
+- model.layers.53.self_attn.o_proj
+- model.layers.54.self_attn.o_proj
+- model.layers.19.self_attn.o_proj
+- model.layers.42.self_attn.o_proj
+- model.layers.49.self_attn.o_proj
+- model.layers.41.self_attn.o_proj
+- model.layers.68.self_attn.o_proj
+- model.layers.18.self_attn.o_proj
+- model.layers.45.self_attn.o_proj
+- model.layers.11.self_attn.o_proj
+- model.layers.67.self_attn.o_proj
+- model.layers.48.self_attn.o_proj
+- model.layers.51.self_attn.o_proj
+- model.layers.64.self_attn.o_proj
+- model.layers.13.self_attn.o_proj
+- model.layers.14.self_attn.o_proj
+- model.layers.16.self_attn.o_proj
+- model.layers.17.self_attn.o_proj
+- model.layers.47.self_attn.o_proj
+- model.layers.0.self_attn.o_proj
+- model.layers.20.self_attn.o_proj
+- model.layers.63.self_attn.o_proj
+- model.layers.15.self_attn.o_proj
+- model.layers.5.self_attn.o_proj
+- model.layers.21.self_attn.o_proj
+- model.layers.52.self_attn.o_proj
+- model.layers.12.self_attn.o_proj
+- model.layers.10.self_attn.o_proj
+- model.layers.62.self_attn.o_proj
+- model.layers.56.self_attn.o_proj
+- model.layers.22.self_attn.o_proj
+- model.layers.6.self_attn.o_proj
+- model.layers.7.self_attn.o_proj
+# self_attn.q_proj layers
+- model.layers.2.self_attn.q_proj
+- model.layers.4.self_attn.q_proj
+- model.layers.46.self_attn.q_proj
+- model.layers.5.self_attn.q_proj
+- model.layers.7.self_attn.q_proj
+- model.layers.6.self_attn.q_proj
+- model.layers.9.self_attn.q_proj
+- model.layers.10.self_attn.q_proj
+- model.layers.1.self_attn.q_proj
+- model.layers.18.self_attn.q_proj
+- model.layers.62.self_attn.q_proj
+- model.layers.8.self_attn.q_proj
+- model.layers.15.self_attn.q_proj
+- model.layers.14.self_attn.q_proj
+- model.layers.16.self_attn.q_proj
+- model.layers.31.self_attn.q_proj
+- model.layers.19.self_attn.q_proj
+- model.layers.17.self_attn.q_proj
+- model.layers.33.self_attn.q_proj
+- model.layers.35.self_attn.q_proj
+- model.layers.12.self_attn.q_proj
+- model.layers.21.self_attn.q_proj
+- model.layers.27.self_attn.q_proj
+- model.layers.34.self_attn.q_proj
+- model.layers.13.self_attn.q_proj
+- model.layers.56.self_attn.q_proj
+- model.layers.11.self_attn.q_proj
+- model.layers.52.self_attn.q_proj
+- model.layers.54.self_attn.q_proj
+- model.layers.28.self_attn.q_proj
+- model.layers.30.self_attn.q_proj
+- model.layers.20.self_attn.q_proj
+- model.layers.29.self_attn.q_proj
+- model.layers.37.self_attn.q_proj
+- model.layers.23.self_attn.q_proj
+- model.layers.75.self_attn.q_proj
+# self_attn.v_proj layers
+- model.layers.11.self_attn.v_proj
+- model.layers.17.self_attn.v_proj
+- model.layers.37.self_attn.v_proj
+- model.layers.40.self_attn.v_proj
+- model.layers.41.self_attn.v_proj
+- model.layers.42.self_attn.v_proj
+- model.layers.43.self_attn.v_proj
+- model.layers.44.self_attn.v_proj
+- model.layers.45.self_attn.v_proj
+- model.layers.46.self_attn.v_proj
+- model.layers.48.self_attn.v_proj
+- model.layers.49.self_attn.v_proj
+- model.layers.50.self_attn.v_proj
+- model.layers.51.self_attn.v_proj
+- model.layers.53.self_attn.v_proj
+- model.layers.54.self_attn.v_proj
+- model.layers.55.self_attn.v_proj
+- model.layers.57.self_attn.v_proj
+- model.layers.58.self_attn.v_proj
+- model.layers.59.self_attn.v_proj
+- model.layers.60.self_attn.v_proj
+- model.layers.61.self_attn.v_proj
+- model.layers.62.self_attn.v_proj
+- model.layers.63.self_attn.v_proj
+- model.layers.64.self_attn.v_proj
+- model.layers.65.self_attn.v_proj
+- model.layers.66.self_attn.v_proj
+- model.layers.67.self_attn.v_proj
+- model.layers.69.self_attn.v_proj
+- model.layers.75.self_attn.v_proj
+- model.layers.18.self_attn.v_proj
+- model.layers.78.self_attn.v_proj
+- model.layers.68.self_attn.v_proj
+- model.layers.47.self_attn.v_proj
+- model.layers.38.self_attn.v_proj
+- model.layers.71.self_attn.v_proj
+# model.norm layers
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: /workspace/axolotl/llama-70b
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+wandb_project: llama-3
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 3
+optimizer: adamw_8bit
+lr_scheduler: cosine
+learning_rate: 1e-5
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: true
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+warmup_steps: 5
+evals_per_epoch: 4
+eval_table_size:
+saves_per_epoch: 4
+save_total_limit: 2
+save_steps:
+debug:
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.00
+fsdp:
+fsdp_config:
+special_tokens:
+  eos_token: "<|im_end|>"
+  pad_token: "<|end_of_text|>"
+tokens:
+  - "<|im_start|>"
+  - "<|im_end|>"
+```
+</details><br>
+# workspace/axolotl/llama-70b
+This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4808
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 64
+- total_eval_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 5
+- num_epochs: 3
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.7659        | 0.0004 | 1    | 0.7454          |
+| 0.5006        | 0.2501 | 587  | 0.4817          |
+| 0.4807        | 0.5002 | 1174 | 0.4698          |
+| 0.4758        | 0.7503 | 1761 | 0.4627          |
+| 0.4969        | 1.0004 | 2348 | 0.4558          |
+| 0.3604        | 1.2346 | 2935 | 0.4635          |
+| 0.3817        | 1.4847 | 3522 | 0.4572          |
+| 0.377         | 1.7348 | 4109 | 0.4533          |
+| 0.3695        | 1.9849 | 4696 | 0.4487          |
+| 0.2676        | 2.2187 | 5283 | 0.4825          |
+| 0.255         | 2.4688 | 5870 | 0.4814          |
+| 0.2851        | 2.7189 | 6457 | 0.4808          |
+### Framework versions
+- Transformers 4.40.2
+- Pytorch 2.2.2+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1