Instructions to use syzymon/long_llama_code_7b_instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use syzymon/long_llama_code_7b_instruct with Transformers:

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

pipe = pipeline("text-generation", model="syzymon/long_llama_code_7b_instruct", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("syzymon/long_llama_code_7b_instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use syzymon/long_llama_code_7b_instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "syzymon/long_llama_code_7b_instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "syzymon/long_llama_code_7b_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/syzymon/long_llama_code_7b_instruct

SGLang

How to use syzymon/long_llama_code_7b_instruct 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 "syzymon/long_llama_code_7b_instruct" \
    --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": "syzymon/long_llama_code_7b_instruct",
		"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 "syzymon/long_llama_code_7b_instruct" \
        --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": "syzymon/long_llama_code_7b_instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use syzymon/long_llama_code_7b_instruct with Docker Model Runner:
```
docker model run hf.co/syzymon/long_llama_code_7b_instruct
```

long_llama_code_7b_instruct

File size: 1,096 Bytes

c4c0e44

{
  "architectures": [
    "LongLlamaForCausalLM"
  ],
  "bos_token_id": 1,
  "eos_token_id": 2,
  "gradient_checkpoint_every_ith": 1,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "last_context_length": 2048,
  "max_position_embeddings": 4096,
  "mem_attention_grouping": null,
  "mem_dtype": "bfloat16",
  "mem_layers": [
    8,
    16,
    24
  ],
  "mem_positionals": true,
  "model_type": "longllama",
  "num_attention_heads": 32,
  "num_hidden_layers": 32,
  "pad_token_id": 0,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "tie_word_embeddings": false,
  "torch_attention": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.30.0",
  "use_cache": true,
  "vocab_size": 32016,
  "auto_map": {
    "AutoConfig": "configuration_longllama.LongLlamaConfig",
    "AutoModel": "modeling_longllama.LongLlamaModel",
    "AutoModelForCausalLM": "modeling_longllama.LongLlamaForCausalLM",
    "AutoModelForSequenceClassification": "modeling_longllama.LongLlamaForSequenceClassification"
  }
}