Instructions to use aswain4/custom_coding_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aswain4/custom_coding_LLM with Transformers:

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

pipe = pipeline("text-generation", model="aswain4/custom_coding_LLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aswain4/custom_coding_LLM")
model = AutoModelForCausalLM.from_pretrained("aswain4/custom_coding_LLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aswain4/custom_coding_LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aswain4/custom_coding_LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aswain4/custom_coding_LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aswain4/custom_coding_LLM

SGLang

How to use aswain4/custom_coding_LLM 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 "aswain4/custom_coding_LLM" \
    --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": "aswain4/custom_coding_LLM",
		"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 "aswain4/custom_coding_LLM" \
        --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": "aswain4/custom_coding_LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aswain4/custom_coding_LLM with Docker Model Runner:
```
docker model run hf.co/aswain4/custom_coding_LLM
```

custom_coding_LLM / adapter_config.json

aswain4

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	{
	"alpha_pattern": {},
	"auto_mapping": null,
	"base_model_name_or_path": "mistralai/Mistral-7B-v0.1",
	"bias": "none",
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": false,
	"inference_mode": true,
	"init_lora_weights": true,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 64,
	"lora_bias": false,
	"lora_dropout": 0.05,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"r": 64,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"q_proj",
	"v_proj"
	],
	"task_type": "CAUSAL_LM",
	"use_dora": false,
	"use_rslora": false
	}