Text Generation
Transformers
Safetensors
English
llama
causal-lm
instruct
chat
decoder-only
autoregressive
from-scratch
retro
1980s
usenet
magazines
books
computer-history
english
text-generation-inference
Instructions to use guus4324343/Echo88-150M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guus4324343/Echo88-150M-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="guus4324343/Echo88-150M-Instruct")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("guus4324343/Echo88-150M-Instruct") model = AutoModelForCausalLM.from_pretrained("guus4324343/Echo88-150M-Instruct") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use guus4324343/Echo88-150M-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "guus4324343/Echo88-150M-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guus4324343/Echo88-150M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/guus4324343/Echo88-150M-Instruct
- SGLang
How to use guus4324343/Echo88-150M-Instruct 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 "guus4324343/Echo88-150M-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guus4324343/Echo88-150M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "guus4324343/Echo88-150M-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guus4324343/Echo88-150M-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use guus4324343/Echo88-150M-Instruct with Docker Model Runner:
docker model run hf.co/guus4324343/Echo88-150M-Instruct
Upload folder using huggingface_hub
Browse files- config.json +32 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- sft_training_info.json +12 -0
- tokenizer.json +0 -0
- tokenizer_config.json +17 -0
- training_args.bin +3 -0
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 2048,
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"max_position_embeddings": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 12,
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"num_hidden_layers": 18,
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"num_key_value_heads": 4,
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"pad_token_id": 1,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.7.0",
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"use_cache": false,
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"vocab_size": 32768
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 1,
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"transformers_version": "5.7.0",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:129f111d87350f7968e793066a21b2592bc61cc56c6e6431c3d48de2effd3080
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size 327231072
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sft_training_info.json
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{
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"base_model": "/content/echo88_final/Echo88-150M-Instruct",
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"instruct_dataset": "/content/echo88/echo88_fact_repair_20k.jsonl",
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"max_length": 1024,
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"micro_batch": 64,
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"grad_accum": 2,
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"epochs": 4,
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"learning_rate": 1e-05,
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"train_examples": 19500,
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"eval_examples": 500,
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"system_prompt": "You are Echo88, a helpful computer assistant whose records go up to the end of 1988. Answer clearly. Do not pretend to know events, products, or culture after 1988."
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}
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"extra_special_tokens": [
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"<|system|>",
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"<|user|>",
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"<|assistant|>",
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"<|end|>"
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],
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"is_local": true,
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"local_files_only": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<|unk|>"
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:75a742724ffcbdc1864877d7b22149c4eaa266267320603881764b14f1172669
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size 5265
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