LFM2.5-1.2B-Instruct-8bit
MLX export of LFM2.5-1.2B-Instruct for Apple Silicon inference.
Model Details
| Property | Value |
|---|---|
| Parameters | 1.2B |
| Precision | 8-bit |
| Group Size | 64 |
| Context Length | 128K |
Recommended Sampling Parameters
| Parameter | Value |
|---|---|
| temperature | 0.1 |
| top_k | 50 |
| top_p | 0.1 |
| repetition_penalty | 1.05 |
| max_tokens | 512 |
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors
model, tokenizer = load("LiquidAI/LFM2.5-1.2B-Instruct-8bit")
prompt = "What is the capital of France?"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
sampler = make_sampler(temp=0.1, top_k=50, top_p=0.1)
logits_processors = make_logits_processors(repetition_penalty=1.05)
response = generate(
model,
tokenizer,
prompt=prompt,
max_tokens=512,
sampler=sampler,
logits_processors=logits_processors,
verbose=True,
)
License
This model is released under the LFM 1.0 License.
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Model size
1B params
Tensor type
U32
·
BF16 ·
Hardware compatibility
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8-bit
Model tree for LiquidAI/LFM2.5-1.2B-Instruct-MLX-8bit
Base model
LiquidAI/LFM2.5-1.2B-Base Finetuned
LiquidAI/LFM2.5-1.2B-Instruct
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("LiquidAI/LFM2.5-1.2B-Instruct-MLX-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True)