jlesbegu/evalllm2025

jlesbegu/ evalllm2025_v2

26 Downloads Updated 10 months ago

finetuned : python -m mlx_lm.lora --train --model mistralai/Mistral-7B-Instruct-v0.3 --batch-size 4 --fine-tune-type dora --iters 1000 --num-layers 16

tools

ollama run jlesbegu/evalllm2025_v2

curl http://localhost:11434/api/chat \
  -d '{
    "model": "jlesbegu/evalllm2025_v2",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='jlesbegu/evalllm2025_v2',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'jlesbegu/evalllm2025_v2',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Applications

Claude Code ollama launch claude --model jlesbegu/evalllm2025_v2

Codex ollama launch codex --model jlesbegu/evalllm2025_v2

OpenCode ollama launch opencode --model jlesbegu/evalllm2025_v2

OpenClaw ollama launch openclaw --model jlesbegu/evalllm2025_v2

Models

View all →

Name

1 model

Size

Context

Input

evalllm2025_v2:latest

14GB · 32K context window · Text · 10 months ago

evalllm2025_v2:latest

14GB

32K

Text

Readme

finetuned with the command: python -m mlx_lm.lora --train --model mistralai/Mistral-7B-Instruct-v0.3 --batch-size 4 --fine-tune-type dora --iters 1000 --num-layers 16