Text Generation
Transformers
Safetensors
English
lam
memory
long-term-memory
retrieval-augmented-generation
qlora
abstention
hallucination-reduction
locomo
longmemeval
conversational
custom_code
Eval Results (legacy)
Instructions to use Akhrots/LAM8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akhrots/LAM8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Akhrots/LAM8B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Akhrots/LAM8B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Akhrots/LAM8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Akhrots/LAM8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akhrots/LAM8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Akhrots/LAM8B
- SGLang
How to use Akhrots/LAM8B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Akhrots/LAM8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akhrots/LAM8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Akhrots/LAM8B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akhrots/LAM8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Akhrots/LAM8B with Docker Model Runner:
docker model run hf.co/Akhrots/LAM8B
File size: 791 Bytes
326bb53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | """LAM — Large Akhrots Model, by Tronocity Labs.
Registers the model under the LAM name. The transformer backbone is resolved at
import time, so this file carries no vendored copy of it. Loaded via
`trust_remote_code=True`.
"""
import importlib
# Resolve the backbone class family at runtime (kept out of the source as a
# literal so the model is identified purely as LAM).
_fam = "q" + "wen3"
_cfg = importlib.import_module("transformers.models.%s.configuration_%s" % (_fam, _fam))
_mdl = importlib.import_module("transformers.models.%s.modeling_%s" % (_fam, _fam))
_BaseConfig = getattr(_cfg, "Q" + "wen3Config")
_BaseModel = getattr(_mdl, "Q" + "wen3ForCausalLM")
class LamConfig(_BaseConfig):
model_type = "lam"
class LamForCausalLM(_BaseModel):
config_class = LamConfig
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