Instructions to use chargoddard/llama-2-34b-uncode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/llama-2-34b-uncode with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/llama-2-34b-uncode")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/llama-2-34b-uncode")
model = AutoModelForCausalLM.from_pretrained("chargoddard/llama-2-34b-uncode")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use chargoddard/llama-2-34b-uncode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chargoddard/llama-2-34b-uncode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/llama-2-34b-uncode",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chargoddard/llama-2-34b-uncode

SGLang

How to use chargoddard/llama-2-34b-uncode 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 "chargoddard/llama-2-34b-uncode" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/llama-2-34b-uncode",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "chargoddard/llama-2-34b-uncode" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chargoddard/llama-2-34b-uncode",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/llama-2-34b-uncode with Docker Model Runner:
```
docker model run hf.co/chargoddard/llama-2-34b-uncode
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

very wip experiment.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	36.2
ARC (25-shot)	39.51
HellaSwag (10-shot)	33.9
MMLU (5-shot)	38.49
TruthfulQA (0-shot)	40.94
Winogrande (5-shot)	74.35
GSM8K (5-shot)	20.77
DROP (3-shot)	5.43

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Safetensors

Model size

34B params

Tensor type

F16

Model tree for chargoddard/llama-2-34b-uncode

Quantizations

2 models

chargoddard
/

llama-2-34b-uncode

Open LLM Leaderboard Evaluation Results

Model tree for chargoddard/llama-2-34b-uncode

Dataset used to train chargoddard/llama-2-34b-uncode

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