Instructions to use KunalNath/CodingGuru-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KunalNath/CodingGuru-1B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/content/drive/MyDrive/StarCoderBase-1B")
model = PeftModel.from_pretrained(base_model, "KunalNath/CodingGuru-1B")

Transformers

How to use KunalNath/CodingGuru-1B with Transformers:

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

pipe = pipeline("text-generation", model="KunalNath/CodingGuru-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KunalNath/CodingGuru-1B")
model = AutoModelForCausalLM.from_pretrained("KunalNath/CodingGuru-1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KunalNath/CodingGuru-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KunalNath/CodingGuru-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KunalNath/CodingGuru-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KunalNath/CodingGuru-1B

SGLang

How to use KunalNath/CodingGuru-1B 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 "KunalNath/CodingGuru-1B" \
    --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": "KunalNath/CodingGuru-1B",
		"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 "KunalNath/CodingGuru-1B" \
        --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": "KunalNath/CodingGuru-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KunalNath/CodingGuru-1B with Docker Model Runner:
```
docker model run hf.co/KunalNath/CodingGuru-1B
```

CodingGuru-1B

File size: 986 Bytes

df32177

{
  "activation_function": "gelu_pytorch_tanh",
  "architectures": [
    "GPTBigCodeForCausalLM"
  ],
  "attention_softmax_in_fp32": true,
  "attn_pdrop": 0.1,
  "bos_token_id": 0,
  "dtype": "float32",
  "embd_pdrop": 0.1,
  "eos_token_id": 0,
  "inference_runner": 0,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "max_batch_size": null,
  "max_sequence_length": null,
  "model_type": "gpt_bigcode",
  "multi_query": true,
  "n_embd": 2048,
  "n_head": 16,
  "n_inner": 8192,
  "n_layer": 24,
  "n_positions": 8192,
  "num_key_value_heads": 1,
  "pad_key_length": true,
  "pre_allocate_kv_cache": false,
  "resid_pdrop": 0.1,
  "scale_attention_softmax_in_fp32": true,
  "scale_attn_weights": true,
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "transformers_version": "4.57.1",
  "use_cache": true,
  "validate_runner_input": true,
  "vocab_size": 49152
}