Instructions to use CarperAI/pythia-6.9b-deduped-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CarperAI/pythia-6.9b-deduped-4k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CarperAI/pythia-6.9b-deduped-4k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CarperAI/pythia-6.9b-deduped-4k")
model = AutoModelForCausalLM.from_pretrained("CarperAI/pythia-6.9b-deduped-4k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CarperAI/pythia-6.9b-deduped-4k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CarperAI/pythia-6.9b-deduped-4k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CarperAI/pythia-6.9b-deduped-4k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CarperAI/pythia-6.9b-deduped-4k

SGLang

How to use CarperAI/pythia-6.9b-deduped-4k 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 "CarperAI/pythia-6.9b-deduped-4k" \
    --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": "CarperAI/pythia-6.9b-deduped-4k",
		"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 "CarperAI/pythia-6.9b-deduped-4k" \
        --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": "CarperAI/pythia-6.9b-deduped-4k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CarperAI/pythia-6.9b-deduped-4k with Docker Model Runner:
```
docker model run hf.co/CarperAI/pythia-6.9b-deduped-4k
```

Pythia-6.9B Deduped 4K is a Pythia-6.9B Deduped model fine-tuned with a 4096 context length. Training resumed from their 143,000 step checkpoint and continued on The Pile v1 Deduped (threshold=0.87). This particular model is from a checkpoint captured at step 175,500 for an extra 134,217,728,000 tokens of training.

Note: Sequence length warmup was not used to move up from 2048 but, in hindsight, should have been applied.

Config

{
  # 16 Nodes 8xA100 40GB
  "optimizer": {
    "type": "Adam",
    "params": {
      "lr": 1.2e-5,
      "betas": [0.9, 0.95],
      "eps": 1.0e-08
    },
  },
  "min_lr": 6.0e-6,

  "pipe-parallel-size": 1,
  "model-parallel-size": 2,

  "num-layers": 32,
  "hidden-size": 4096,
  "num-attention-heads": 32,
  "seq-length": 4096,
  "max-position-embeddings": 4096,

  "norm": "layernorm",
  "pos-emb": "rotary",
  "rotary_pct": 0.25,
  "no-weight-tying": true,
  "gpt_j_residual": true,
  "output_layer_parallelism": "column",

  "attention-config": [[["flash"], 32]],
  "scaled-upper-triang-masked-softmax-fusion": true,
  "bias-gelu-fusion": true,

  "zero_optimization": {
    "stage": 1,
    "allgather_partitions": true,
    "allgather_bucket_size": 1260000000,
    "overlap_comm": true,
    "reduce_scatter": true,
    "reduce_bucket_size": 1260000000,
    "contiguous_gradients": true,
    "cpu_offload": false,
  },

  "train_micro_batch_size_per_gpu": 8,
  "eval_batch_size": 2,
  "gradient_accumulation_steps": 2,
  "data-impl": "mmap",
  
  "checkpoint-activations": true,
  "checkpoint-num-layers": 1,
  "partition-activations": true,
  "synchronize-each-layer": true,

  "gradient_clipping": 1.0,
  "weight-decay": 0.1,
  "hidden-dropout": 0,
  "attention-dropout": 0,

  "fp16": {
    "fp16": true,
    "enabled": true,
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "initial_scale_power": 12,
    "hysteresis": 2,
    "min_loss_scale": 1,
  },

  "train-iters": 318000, 
  "lr-decay-iters": 318000,
  "distributed-backend": "nccl",
  "lr-decay-style": "cosine",
  "warmup": 0.01,
  "checkpoint-factor": 500,
  "eval-interval": 50000,
  "eval-iters": 10,
  "extra-save-iters": [0, 512, 149001],

  "train-data-paths": ["pile_0.87_deduped_text_document"],
  "valid-data-paths": ["pile_0.87_deduped_text_document"],
  "test-data-paths": ["pile_0.87_deduped_text_document"],

  "tokenizer_type": "HFTokenizer",
  "vocab-file": "20B_tokenizer.json",

  "log-interval": 10,
  "steps_per_print": 10,
  "wall_clock_breakdown": true,
  "log-grad-norm": true,

  "launcher": "slurm",
  "deepspeed_slurm": true,
}

Acknoweldgements

This work would not have been possible without the support of Stability AI.

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pythia-6.9b-deduped-4k

Config

Acknoweldgements

Dataset used to train CarperAI/pythia-6.9b-deduped-4k