Instructions to use sriksven/ExtractIQ-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sriksven/ExtractIQ-7B with Transformers:

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

pipe = pipeline("text-generation", model="sriksven/ExtractIQ-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sriksven/ExtractIQ-7B")
model = AutoModelForCausalLM.from_pretrained("sriksven/ExtractIQ-7B")
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 sriksven/ExtractIQ-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sriksven/ExtractIQ-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sriksven/ExtractIQ-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sriksven/ExtractIQ-7B

SGLang

How to use sriksven/ExtractIQ-7B 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 "sriksven/ExtractIQ-7B" \
    --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": "sriksven/ExtractIQ-7B",
		"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 "sriksven/ExtractIQ-7B" \
        --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": "sriksven/ExtractIQ-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use sriksven/ExtractIQ-7B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sriksven/ExtractIQ-7B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sriksven/ExtractIQ-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sriksven/ExtractIQ-7B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="sriksven/ExtractIQ-7B",
    max_seq_length=2048,
)

Docker Model Runner
How to use sriksven/ExtractIQ-7B with Docker Model Runner:
```
docker model run hf.co/sriksven/ExtractIQ-7B
```

sriksven commited on 1 day ago

Commit

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1 Parent(s): 4ab92f9

Create README.md

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	@@ -0,0 +1,140 @@

+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - code
+  - code-review
+  - programming
+  - qlora
+  - unsloth
+  - qwen2.5
+  - bug-detection
+datasets:
+  - sahil2801/CodeAlpaca-20k
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+model-index:
+  - name: CodeLens-7B
+    results: []
+---
+# CodeLens-7B
+A fine-tuned **Qwen2.5-7B-Instruct** model specialized for **code review, bug detection, and programming assistance**. It analyzes code snippets, identifies issues, suggests improvements, and writes clean solutions across multiple programming languages.
+## Key Details
+| | |
+|---|---|
+| **Base model** | Qwen/Qwen2.5-7B-Instruct |
+| **Method** | QLoRA (4-bit NF4, rank 16, alpha 16) |
+| **Library** | Unsloth + TRL SFTTrainer |
+| **Dataset** | sahil2801/CodeAlpaca-20k (10K examples) |
+| **Hardware** | NVIDIA RTX A5000 (24GB VRAM) on RunPod |
+| **Training time** | ~2.65 hours (500 steps) |
+| **Final loss** | 0.450 |
+| **Parameters trained** | 40.4M of 7.66B (0.53%) |
+| **Format** | ChatML |
+| **Output** | Merged 16-bit safetensors |
+## Dataset
+Trained on 10,000 examples from [sahil2801/CodeAlpaca-20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k), a code instruction-following dataset covering code generation, debugging, explanation, and review tasks across Python, JavaScript, Java, C, SQL, and more.
+## Usage
+### Transformers
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("sriksven/CodeLens-7B")
+tokenizer = AutoTokenizer.from_pretrained("sriksven/CodeLens-7B")
+messages = [
+    {
+        "role": "system",
+        "content": "You are an expert code reviewer and programmer. Analyze code, find bugs, suggest improvements, and write clean efficient solutions.",
+    },
+    {
+        "role": "user",
+        "content": "Review this Python function for bugs and improvements:\n\ndef find_duplicates(lst):\n    seen = []\n    dupes = []\n    for i in lst:\n        if i in seen:\n            dupes.append(i)\n        seen.append(i)\n    return dupes",
+    },
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+outputs = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+### Unsloth (faster inference)
+```python
+from unsloth import FastLanguageModel
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="sriksven/CodeLens-7B",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+```
+## Capabilities
+- **Code review** — analyze code for bugs, anti-patterns, and style issues
+- **Bug detection** — identify logical errors, off-by-one mistakes, edge cases
+- **Code generation** — write functions, classes, and scripts from descriptions
+- **Code explanation** — explain what a piece of code does step by step
+- **Refactoring suggestions** — propose cleaner, more efficient alternatives
+- **Multi-language** — Python, JavaScript, Java, C/C++, SQL, HTML/CSS, and more
+## Intended Use
+- Local code review assistant
+- Programming tutoring and education
+- Code quality tooling in CI/CD pipelines
+- Prototyping developer tools with local LLMs
+## Limitations
+- Trained on instruction-following code data, not real code review conversations from PRs
+- May not catch security vulnerabilities that require deep context
+- Code suggestions should be tested before use in production
+- Best with shorter code snippets (functions/classes) rather than full files
+- No execution or testing capability — suggestions are pattern-based
+## Training Metrics
+Loss decreased steadily from 2.17 to 0.27 over 500 steps (~13 epochs), indicating strong learning on the code instruction data.
+| Step | Loss | Epoch |
+|---|---|---|
+| 10 | 2.168 | 0.26 |
+| 100 | 0.503 | 2.05 |
+| 250 | 0.430 | 6.41 |
+| 400 | 0.310 | 10.26 |
+| 500 | 0.278 | 12.83 |
+## Training Infrastructure
+| | |
+|---|---|
+| **GPU** | NVIDIA RTX A5000 24GB |
+| **Cloud** | RunPod ($0.27/hr) |
+| **Framework** | Unsloth 2026.5.2 + TRL + Transformers 5.5.0 |
+| **Precision** | BF16 training, 4-bit NF4 base quantization |
+| **Optimizer** | AdamW 8-bit |
+| **Learning rate** | 2e-4, linear decay |
+| **Batch size** | 16 effective (4 per device × 4 accumulation) |
+| **Packing** | Enabled |
+## Source Code
+Training scripts: [github.com/sriksven/LLM-FineTune-Suite](https://github.com/sriksven/LLM-FineTune-Suite)
+## License
+Apache 2.0