Text Generation
MLX
Safetensors
English
qwen2
code
chat
microsoft
nextcoder
selekt
conversational
4-bit precision
How to use from
MLX LMRun an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm# Start the server
mlx_lm.server --model "jedisct1/NextCoder-14B-q4-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "jedisct1/NextCoder-14B-q4-mlx",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Quick Links
jedisct1/NextCoder-14B-mlx
This model jedisct1/NextCoder-14B-mlx was converted to MLX format from microsoft/NextCoder-14B using mlx-lm version 0.25.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("jedisct1/NextCoder-14B-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
15B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Model tree for jedisct1/NextCoder-14B-q4-mlx
Base model
Qwen/Qwen2.5-14B Finetuned
Qwen/Qwen2.5-Coder-14B Finetuned
Qwen/Qwen2.5-Coder-14B-Instruct Finetuned
microsoft/NextCoder-14B
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm# Interactive chat REPL mlx_lm.chat --model "jedisct1/NextCoder-14B-q4-mlx"