Instinct-5-1.3B

Instinct 1.4B

autonomousX/instinct-1.4B on Hugging Face

Training scripts and Logs on GITHUB

instinct-1.4B is a fully reproducible, from-scratch trained 1.4B parameter language model trained on 70B tokens using TPU v4 infrastructure.

instinct-1.4B is a 1.4B parameter Large Language Model built entirely from scratch under the AutonomousX organization.

Compute for this project was supported by Google's TRC Program (TPU Research Cloud).

---

👨‍💻 Author Information

Rohit Yadav B.Tech 3rd Year
Dr. B.R. Ambedkar National Institute of Technology (NIT) Jalandhar, India
E-mail: [email protected]
LinkedIn: Rohit Yadav
GitHub: YADAV1825

Research interests include: Large Language Models, MultiModal Pipelines, Systems Programming, AI Infrastructure, Distributed Training.

---

About AutonomousX

AutonomousX focuses on open-source contributions aimed at building Large Language Models from scratch using custom training pipelines. Our work explores different training configurations including optimizers, datasets, and scalable TPU training using JAX and pmap. The goal is to provide transparent and reproducible implementations so that researchers, students, and developers can understand how modern LLMs are trained end-to-end.

Due to the current scarcity of complete beginner-friendly guides for training LLMs on TPUs, especially using JAX, AutonomousX aims to bridge this gap by publishing full training pipelines, scripts, and documentation for the open-source community.

Maintained by: Rohit Yadav | B.Tech NIT Jalandhar | [email protected] | Hugging_Face

---

⚠️ Disclaimer

This is a base model, not an SFT (Supervised Fine-Tuned) or RLHF (Reinforcement Learning from Human Feedback) model. As a raw completion model, it may output undesired, biased, or nonsensical text. It is intended primarily for research and educational purposes.

---

📊 Model Overview

---

🧠 Training Details

instinct-1.4B was trained completely from scratch using JAX/Flax on TPU v4-8 hardware.

The training pipeline includes:

• Dataset streaming from DOLMA.
• Pythia Tokenizer with a 50,304 vocabulary size.
• TPU optimized JAX / Flax training loop.
• Lion optimizer for stable convergence.
• Checkpointing and validation during training.
• Rolling validation shard evaluation.

🔄 Training Workflow

graph TD
    %% Dataset and Preparation
    Data["Dataset: DOLMA\nRaw Text Data"]
    Tokenizer["BPE Tokenizer\nVocabulary Construction"]
    TokenizedData["Tokenized Data\nReady for Training"]

    %% Model Architecture
    Model["v-4 (128) pmap\nTransformer Decoder\n1.4B Parameters"]
    RoPE["RoPE Positional Embeddings"]

    %% Training Pipeline
    Optimizer["Optimizer: Lion"]
    ForwardPass["Forward Pass\nCompute Loss"]
    BackwardPass["Backward Pass\nCompute Gradients"]
    Update["Parameter Update"]

    %% Logging and Checkpoints
    Checkpoints["Model Checkpoints\nSaved up to 70B tokens"]
    Logs["Training Logs\nLoss & Perplexity"]

    %% Connections
    Data --> Tokenizer
    Tokenizer --> TokenizedData
    TokenizedData --> ForwardPass
    
    Model --> ForwardPass
    RoPE -.-> Model

    ForwardPass --> BackwardPass
    BackwardPass --> Optimizer
    Optimizer --> Update
    Update --> Model

    Update --> Checkpoints
    Update --> Logs

📈 Training Curves

The loss curves are saved in training_log.txt and val_perplexity.txt. Below is the visualization of the training progress:

---

🔄 Reproducibility

The entire pipeline used to train the model is fully reproducible. This includes the dataset pipeline, tokenizer creation, model architecture, TPU training loop, and checkpointing system.

Full training pipeline repository: [train.py](https://github.com/YADAV1825/Instinct 1.4B/blob/main/train.py) (Example link format)

---

🚀 Run Inference (Colab TPU/GPU)

The trained LLM inference script and model weights are available at: autonomousX/instinct-1.4B on Hugging Face.

⚠️ Disclaimer: This is a sample inference script originally written for the 0.5B (40B) variants. Please adjust parameters like N_LAYERS, D_MODEL, N_HEADS, D_HEAD, D_FF, etc., according to this specific model's architecture (1.4B) to run inference successfully.

A ready-to-run Google Colab TPU/GPU inference script is provided below. Simply open a notebook, set your runtime to TPU or GPU, and run it. (Please be patient, it may take around 20 mins to run the model initialization).

<details> <summary>Click here to view the full Inference Code</summary>

#please be patient It may take 20 mins to run the model
# Install huggingface_hub if not installed
!pip install -q huggingface_hub

from huggingface_hub import snapshot_download

repo_id = "autonomousX/instinct-1.4B"

# Download entire repository
local_path = snapshot_download(
    repo_id=repo_id,
    repo_type="model",
    local_dir="TPU_1.4B",
    local_dir_use_symlinks=False
)

print("Download complete!")
print("Saved to:", local_path)

# =========================
# FAST 1.4B INFERENCE CELL
# =========================

import os
import math
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.training import train_state, checkpoints
import optax
from transformers import AutoTokenizer

# ---------------- CONFIG ----------------
SEQ_LEN = 1024
VOCAB_SIZE = 50304

# NOTE: Adjust these parameters for your specific 1.4B architecture!
N_LAYERS = 32
D_MODEL = 1024
N_HEADS = 16
D_HEAD = 64
D_FF = 4096
ROTARY_PCT = 0.25

CKPT_PATH = os.path.abspath("TPU_1.4B/checkpoint_0")

# ---------------- RoPE ----------------
def build_rope_cache(seq_len, head_dim, rotary_pct):
    dim = int(head_dim * rotary_pct)
    freqs = 1.0 / (10000 ** (jnp.arange(0, dim, 2) / dim))
    pos = jnp.arange(seq_len)
    angles = jnp.einsum("i,j->ij", pos, freqs)
    return jnp.sin(angles), jnp.cos(angles)

ROPE_SIN, ROPE_COS = build_rope_cache(SEQ_LEN, D_HEAD, ROTARY_PCT)

def apply_rope(q, k):
    dim = int(D_HEAD * ROTARY_PCT)
    T = q.shape[1]

    sin = ROPE_SIN[:T][None, :, None, :]
    cos = ROPE_COS[:T][None, :, None, :]

    q_rot, q_pass = q[..., :dim], q[..., dim:]
    k_rot, k_pass = k[..., :dim], k[..., dim:]

    q1, q2 = q_rot[..., ::2], q_rot[..., 1::2]
    k1, k2 = k_rot[..., ::2], k_rot[..., 1::2]

    q_rot = jnp.concatenate(
        [q1 * cos - q2 * sin,
         q1 * sin + q2 * cos],
        axis=-1
    )

    k_rot = jnp.concatenate(
        [k1 * cos - k2 * sin,
         k1 * sin + k2 * cos],
        axis=-1
    )

    return (
        jnp.concatenate([q_rot, q_pass], axis=-1),
        jnp.concatenate([k_rot, k_pass], axis=-1),
    )

# ---------------- MODEL ----------------
class RMSNorm(nn.Module):
    dim: int
    eps: float = 1e-6
    @nn.compact
    def __call__(self, x):
        scale = self.param("scale", nn.initializers.ones, (self.dim,))
        norm = jnp.sqrt(jnp.mean(x**2, axis=-1, keepdims=True) + self.eps)
        return x * (scale / norm)

class Attention(nn.Module):
    @nn.compact
    def __call__(self, x, mask):
        B, T, C = x.shape
        qkv = nn.Dense(3 * C, use_bias=False, dtype=jnp.bfloat16)(x)
        qkv = qkv.reshape(B, T, 3, N_HEADS, D_HEAD)

        q = qkv[:, :, 0]
        k = qkv[:, :, 1]
        v = qkv[:, :, 2]

        q, k = apply_rope(q, k)

        att = jnp.einsum("bthd,bshd->bhts", q, k)
        att = att / math.sqrt(D_HEAD)

        mask = mask.astype(jnp.float32)
        mask = (1.0 - mask) * -1e10
        att = att + mask

        att = nn.softmax(att.astype(jnp.float32), axis=-1)
        att = att.astype(jnp.bfloat16)

        out = jnp.einsum("bhts,bshd->bthd", att, v)
        out = out.reshape(B, T, C)

        return nn.Dense(C, use_bias=False, dtype=jnp.bfloat16)(out)

class Block(nn.Module):
    @nn.compact
    def __call__(self, x, mask):
        h = RMSNorm(D_MODEL)(x)
        h = Attention()(h, mask)
        x = x + h

        h = RMSNorm(D_MODEL)(x)
        h = nn.Dense(D_FF, dtype=jnp.bfloat16)(h)
        h = nn.gelu(h)
        h = nn.Dense(D_MODEL, dtype=jnp.bfloat16)(h)

        return x + h

class GPT(nn.Module):
    @nn.compact
    def __call__(self, input_ids):
        batch, seq_len = input_ids.shape
        mask = nn.attention.make_causal_mask(
            jnp.ones((batch, seq_len), dtype=jnp.bool_)
        )

        x = nn.Embed(
            VOCAB_SIZE,
            D_MODEL,
            embedding_init=nn.initializers.normal(0.02),
            dtype=jnp.bfloat16,
        )(input_ids)

        RematBlock = nn.remat(Block)

        for _ in range(N_LAYERS):
            x = RematBlock()(x, mask)

        x = RMSNorm(D_MODEL)(x)

        return nn.Dense(
            VOCAB_SIZE,
            use_bias=False,
            dtype=jnp.bfloat16
        )(x)
# ---------------- LOAD CHECKPOINT ----------------
def create_state():
    model = GPT()
    rng = jax.random.PRNGKey(0)
    params = model.init(rng, jnp.ones((1, SEQ_LEN), dtype=jnp.int32))
    return train_state.TrainState.create(
        apply_fn=model.apply,
        params=params,
        tx=optax.adamw(1e-4),
    )

state = create_state()
state = checkpoints.restore_checkpoint(CKPT_PATH, state)
params = state.params
model = GPT()

print("Checkpoint loaded.")

@jax.jit
def forward(params, input_ids):
    return model.apply(params, input_ids)

import jax.random as random

def generate(params, input_ids, max_new_tokens=30, temperature=0.9, top_k=40):
    rng = random.PRNGKey(0)

    for _ in range(max_new_tokens):

        logits = model.apply(params, input_ids)
        logits = logits[:, -1, :]
        logits = logits.astype(jnp.float32)

        logits = logits / temperature

        top_k_logits, top_k_indices = jax.lax.top_k(logits, top_k)
        probs = jax.nn.softmax(top_k_logits, axis=-1)

        rng, subkey = random.split(rng)
        next_token_idx = random.categorical(subkey, jnp.log(probs))

        next_token = jnp.take_along_axis(
            top_k_indices,
            next_token_idx[:, None],
            axis=-1
        )

        input_ids = jnp.concatenate([input_ids, next_token], axis=1)

    return input_ids
# ---------------- RUN ----------------
tokenizer = AutoTokenizer.from_pretrained("autonomousX/instinct-1.4B")

prompt = "I am John,"
tokens = tokenizer(prompt, return_tensors="np")
input_ids = jnp.array(tokens["input_ids"], dtype=jnp.int32)

output_ids = generate(params, input_ids, 200)

print("\n=== GENERATED TEXT ===\n")
print(tokenizer.decode(output_ids[0].tolist()))

</details>