DeepSeek-Coder-V2-Lite-Instruct: causal language modeling with Transformers

This page is the Transformers contract of the DeepSeek-Coder-V2-Lite-Instruct MIC and also a usage example. doc-builder renders the runnable blocks below as documentation, and its pytest plugin runs them as written against deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct. The MIC runner runs the same blocks against each slim fixture and each checkpoint in integration.json. Lines marked # doc-builder: hide only matter there: they swap in the pinned checkpoint or variant and print the result record.

Chat completion

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import json  # doc-builder: hide
import os  # doc-builder: hide

model_id = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
model_id = os.environ.get("MIC_MODEL", model_id)  # doc-builder: hide
variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}"))  # doc-builder: hide
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed(0)  # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers

tokenizer = AutoTokenizer.from_pretrained(model_id)
# The checkpoint stores bfloat16 weights; load in float32 so the training step below is stable.
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float32).to(device)
if variant:  # doc-builder: hide
    del model  # doc-builder: hide
    model = AutoModelForCausalLM.from_pretrained(model_id, **{"dtype": torch.float32, **variant}).to(device)  # doc-builder: hide

# The tokenizer has no pad token; use the EOS token and left padding for batched generation.
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.padding_side = "left"

messages_list = [
    [{"role": "user", "content": "Write a Python function that reverses a string."}],
    [{"role": "user", "content": "What is the time complexity of binary search?"}],
]
texts = [tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) for messages in messages_list]
inputs = tokenizer(texts, return_tensors="pt", padding=True).to(device)

with torch.no_grad():
    logits = model(**inputs).logits
    generated = model.generate(**inputs, max_new_tokens=8, do_sample=False)

# Probe the last position of each sequence (left padding, so the last position is the real final token).
probe = logits[:, -1, :5].float().tolist()
generated_text = tokenizer.batch_decode(generated[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(generated_text)

One training step

# Language-model labels: copy input_ids and mask padded positions.
labels = inputs["input_ids"].clone()
labels[inputs["attention_mask"] == 0] = -100

model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
loss = model(**inputs, labels=labels).loss
loss.backward()
before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad}  # doc-builder: hide
optimizer.step()
print(f"loss: {loss.item():.4f}")

if os.environ.get("MIC_RUNNER"):  # doc-builder: hide
    named = dict(model.named_parameters())
    checked = ("model.layers.0.self_attn.q_proj.weight", "model.layers.0.mlp.gate_proj.weight")
    result = {
        "runtime": {
            "device": model.device.type,
            "dtype": str(model.dtype).removeprefix("torch."),
            "attn_implementation": model.config._attn_implementation,
        },
        "logits": {"probe": probe},
        "generated_text": generated_text,
        "training": {
            "loss": loss.item(),
            "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked},
            "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before),
        },
    }
    print(json.dumps(result))