Instructions to use Jashan887/22_IBM_Quantum_Coder_Fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jashan887/22_IBM_Quantum_Coder_Fixed with Transformers:

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

pipe = pipeline("text-generation", model="Jashan887/22_IBM_Quantum_Coder_Fixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jashan887/22_IBM_Quantum_Coder_Fixed")
model = AutoModelForCausalLM.from_pretrained("Jashan887/22_IBM_Quantum_Coder_Fixed")
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 Jashan887/22_IBM_Quantum_Coder_Fixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jashan887/22_IBM_Quantum_Coder_Fixed"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jashan887/22_IBM_Quantum_Coder_Fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jashan887/22_IBM_Quantum_Coder_Fixed

SGLang

How to use Jashan887/22_IBM_Quantum_Coder_Fixed 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 "Jashan887/22_IBM_Quantum_Coder_Fixed" \
    --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": "Jashan887/22_IBM_Quantum_Coder_Fixed",
		"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 "Jashan887/22_IBM_Quantum_Coder_Fixed" \
        --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": "Jashan887/22_IBM_Quantum_Coder_Fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jashan887/22_IBM_Quantum_Coder_Fixed with Docker Model Runner:
```
docker model run hf.co/Jashan887/22_IBM_Quantum_Coder_Fixed
```

22_IBM_Quantum_Coder_Fixed

File size: 645 Bytes

a5d0471

{
  "activation_function": "gelu",
  "architectures": [
    "GPTBigCodeForCausalLM"
  ],
  "attention_softmax_in_fp32": true,
  "attn_pdrop": 0.1,
  "bos_token_id": 0,
  "embd_pdrop": 0.1,
  "eos_token_id": 0,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "model_type": "gpt_bigcode",
  "multi_query": true,
  "n_embd": 6144,
  "n_head": 48,
  "n_inner": 24576,
  "n_layer": 52,
  "n_positions": 8192,
  "pad_token_id": 0,
  "resid_pdrop": 0.1,
  "scale_attention_softmax_in_fp32": true,
  "scale_attn_weights": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "use_cache": false,
  "vocab_size": 49152
}