GGUF Files for dqnCode-v0.4-1.5B-HF
These are the GGUF files for DQN-Labs/dqnCode-v0.4-1.5B-HF.
Downloads
| GGUF Link | Quantization | Description |
|---|---|---|
| Download | Q2_K | Lowest quality |
| Download | Q3_K_S | |
| Download | IQ3_S | Integer quant, preferable over Q3_K_S |
| Download | IQ3_M | Integer quant |
| Download | Q3_K_M | |
| Download | Q3_K_L | |
| Download | IQ4_XS | Integer quant |
| Download | Q4_K_S | Fast with good performance |
| Download | Q4_K_M | Recommended: Perfect mix of speed and performance |
| Download | Q5_K_S | |
| Download | Q5_K_M | |
| Download | Q6_K | Very good quality |
| Download | Q8_0 | Best quality |
| Download | f16 | Full precision, don't bother; use a quant |
Note from Flexan
I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.
If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.
You can find more info about me and what I do here.
DQN-Labs/dqnCode-v0.4-1.5B-HF
This model DQN-Labs/dqnCode-v0.4-1.5B-HF was converted to MLX format from Qwen/Qwen2.5-Coder-1.5B-Instruct using mlx-lm version 0.30.7.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("DQN-Labs/dqnCode-v0.4-1.5B-HF")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for Flexan/DQN-Labs-dqnCode-v0.4-1.5B-HF-GGUF
Base model
Qwen/Qwen2.5-1.5B