Instructions to use altomek/Coding-34B-U6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use altomek/Coding-34B-U6 with Transformers:

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

pipe = pipeline("text-generation", model="altomek/Coding-34B-U6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("altomek/Coding-34B-U6")
model = AutoModelForCausalLM.from_pretrained("altomek/Coding-34B-U6")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use altomek/Coding-34B-U6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "altomek/Coding-34B-U6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "altomek/Coding-34B-U6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/altomek/Coding-34B-U6

SGLang

How to use altomek/Coding-34B-U6 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 "altomek/Coding-34B-U6" \
    --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": "altomek/Coding-34B-U6",
		"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 "altomek/Coding-34B-U6" \
        --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": "altomek/Coding-34B-U6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use altomek/Coding-34B-U6 with Docker Model Runner:
```
docker model run hf.co/altomek/Coding-34B-U6
```

intro music...

Coding-34B-U6-2

Today models are trained on code so much. Have to check how some old ones fare with some assistant bits added ;P

Models Merged

The following models were included in the merge:

Samantha-1.11-CodeLlama-34b
WizardLM-1.0-Uncensored-CodeLlama-34b
CodeBooga-34B-v0.1
CodeLlama-34b-Instruct-hf-abliterated (as base)

Configuration

The following YAML configuration was used to produce this model:

name: Coding-34B-U6-2
models:
  - model: CodeLlama-34b-Instruct-hf-abliterated
  - model: Samantha-1.11-CodeLlama-34b
  - model: WizardLM-1.0-Uncensored-CodeLlama-34b
  - model: CodeBooga-34B-v0.1
base_model: CodeLlama-34b-Instruct-hf-abliterated
merge_method: model_stock
dtype: float16