Instructions to use Locutusque/llama-3-neural-chat-v2.2-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/llama-3-neural-chat-v2.2-8B with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/llama-3-neural-chat-v2.2-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/llama-3-neural-chat-v2.2-8B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/llama-3-neural-chat-v2.2-8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/llama-3-neural-chat-v2.2-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/llama-3-neural-chat-v2.2-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/llama-3-neural-chat-v2.2-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Locutusque/llama-3-neural-chat-v2.2-8B

SGLang

How to use Locutusque/llama-3-neural-chat-v2.2-8B 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 "Locutusque/llama-3-neural-chat-v2.2-8B" \
    --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": "Locutusque/llama-3-neural-chat-v2.2-8B",
		"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 "Locutusque/llama-3-neural-chat-v2.2-8B" \
        --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": "Locutusque/llama-3-neural-chat-v2.2-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Locutusque/llama-3-neural-chat-v2.2-8B with Docker Model Runner:
```
docker model run hf.co/Locutusque/llama-3-neural-chat-v2.2-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

llama-3-neural-chat-v2.2-8b

Model Details

Model Description

I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO-Positive. DPO-Positive dramatically improves performance over DPO.

Developed by: Locutusque
Model type: Built with Meta Llama 3
Language(s) (NLP): Many?
License: Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE

Quants

GGUF: https://huggingface.co/bartowski/llama-3-neural-chat-v2.2-8B-GGUF

ExLlamaV2: https://huggingface.co/bartowski/llama-3-neural-chat-v2.2-8B-exl2

Uses

This model has great performance in writing, coding, and math.

Training Data

Recipe information will be coming soon. This language model's recipe is similar to Intel's Neural Chat.

Direct Use

Conversational AI. This model is also very uncensored, it will respond to pretty much any request regardless of the system prompt, use at your own risk.

Evaluations

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
truthfulqa_mc2	2	none	0	acc	0.5232	±	0.0151
gsm8k	3	strict-match	5	exact_match	0.5974	±	0.0135
		flexible-extract	5	exact_match	0.5974	±	0.0135
agieval_nous	N/A	none	0	acc_norm	0.3841	±	0.0094
		none	0	acc	0.3802	±	0.0094
- agieval_aqua_rat	1	none	0	acc	0.2598	±	0.0276
		none	0	acc_norm	0.2520	±	0.0273
- agieval_logiqa_en	1	none	0	acc	0.3441	±	0.0186
		none	0	acc_norm	0.3687	±	0.0189
- agieval_lsat_ar	1	none	0	acc	0.2217	±	0.0275
		none	0	acc_norm	0.2348	±	0.0280
- agieval_lsat_lr	1	none	0	acc	0.3882	±	0.0216
		none	0	acc_norm	0.3824	±	0.0215
- agieval_lsat_rc	1	none	0	acc	0.4944	±	0.0305
		none	0	acc_norm	0.5019	±	0.0305
- agieval_sat_en	1	none	0	acc	0.6650	±	0.0330
		none	0	acc_norm	0.6553	±	0.0332
- agieval_sat_en_without_passage	1	none	0	acc	0.3981	±	0.0342
		none	0	acc_norm	0.3981	±	0.0342
- agieval_sat_math	1	none	0	acc	0.3500	±	0.0322
		none	0	acc_norm	0.3318	±	0.0318

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Safetensors

Model size

8B params

Tensor type

BF16

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Merges

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llama-3-neural-chat-v2.2-8B