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lhordking
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Model Details
Model Provider
lhordking
Model Tree
Qwen/Qwen2.5-Coder-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base Model | Qwen2.5-Coder-3B-Instruct |
| Method | LoRA (r=16, alpha=32) |
| GPU | AMD Radeon RX 9060 XT (ROCm) |
| Framework | Unsloth + HuggingFace TRL |
| Epochs | 3 |
Python, JavaScript, TypeScript, Rust, Go, SQL, PHP, FastAPI, React, Vue, NestJS, Express, PostgreSQL, Docker
python
from unsloth import FastLanguageModelimport torch model, tokenizer = FastLanguageModel.from_pretrained( model_name = "lhordking/Shadow-coder-v2-LoRA", max_seq_length = 2048, dtype = torch.bfloat16, load_in_4bit = False,)FastLanguageModel.for_inference(model) prompt = ( "### Instruction:\n" "Build a FastAPI endpoint for user authentication with JWT\n\n" "### Context:\n" "Use PostgreSQL and return access + refresh tokens\n\n" "### Response:\n") inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate( **inputs, max_new_tokens = 512, temperature = 0.7, repetition_penalty = 1.3,)print(tokenizer.decode(outputs[0], skip_special_tokens=True))from unsloth import FastLanguageModelimport torch model, tokenizer = FastLanguageModel.from_pretrained( model_name = "lhordking/Shadow-coder-v2-LoRA", max_seq_length = 2048, dtype = torch.bfloat16, load_in_4bit = False,)FastLanguageModel.for_inference(model) prompt = ( "### Instruction:\n" "Build a FastAPI endpoint for user authentication with JWT\n\n" "### Context:\n" "Use PostgreSQL and return access + refresh tokens\n\n" "### Response:\n") inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate( **inputs, max_new_tokens = 512, temperature = 0.7, repetition_penalty = 1.3,)print(tokenizer.decode(outputs[0], skip_special_tokens=True))