talkie-1930-13b-it-mlx-bf16

Talkie 1930 13B Instruct — MLX

MLX port of lewtun/talkie-1930-13b-it-hf for Apple Silicon. Refer to the upstream model card for training-data, evaluation, and provenance details — this card covers only the MLX conversion.

Talkie is a 13B instruction-tuned decoder-only transformer whose outputs are styled as pre-1930s English prose. It uses a custom architecture (custom RoPE convention, weightless RMSNorm, per-head and per-layer scalar gains, embedding-skip residuals, scaled lm_head weights) that is not currently in transformers/models/.

Native Talkie support was added to mlx-lm in PR #1231.

Variants

For 4-bit, prefer the DWQ build. Bare q4 of this model degrades into repetition on long generations; DWQ calibration recovers clean output (validation loss 0.037 vs ≈0.25 for bare q4 in our run).

Installation

pip install -U mlx-lm

Talkie support is in mlx-lm ≥ the version that includes PR #1231. Until released, install from source:

pip install -U git+https://github.com/ml-explore/mlx-lm

Basic generation

from mlx_lm import load, generate

model, tokenizer = load("warshanks/talkie-1930-13b-it-mlx-4bit-DWQ")

messages = [{"role": "user", "content": "Write an essay predicting what life will be like in the year 1960."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

text = generate(model, tokenizer, prompt=prompt, max_tokens=512, verbose=True)

CLI:

mlx_lm.generate \
  --model warshanks/talkie-1930-13b-it-mlx-4bit-DWQ \
  --prompt "<|user|>What were the causes of the French Revolution?<|end|><|assistant|>" \
  --max-tokens 512 --temp 0.7

Multi-turn chat

from mlx_lm import load, generate

model, tokenizer = load("warshanks/talkie-1930-13b-it-mlx-4bit-DWQ")

messages = [{"role": "user", "content": "What were the causes of the French Revolution?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
reply = generate(model, tokenizer, prompt=prompt, max_tokens=512)

messages.append({"role": "assistant", "content": reply})
messages.append({"role": "user", "content": "Which of those causes was the most significant?"})
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=512))

Chat template

<|system|>{system_message}<|end|><|user|>{user_message}<|end|><|assistant|>{assistant_message}<|end|>

Applied automatically by tokenizer.apply_chat_template().

Architecture (as observed in the source checkpoint and modeling code)

Architectural quirks the MLX port reproduces:

• Custom RoPE — formula y1 = x1*cos + x2*sin, y2 = -x1*sin + x2*cos (rotation by −θ, the inverse of the HF/Llama convention). mx.fast.rope is not directly usable; the port ships a small TalkieRoPE class.
• Weightless RMSNorm — applied at the embedding output, before each attention block, before each MLP block, on the post-RoPE Q and K tensors, and before the final lm_head. No learned scale; reduction in fp32 then cast back.
• Per-head Q gain — learnable scalar per attention head applied to queries after RoPE + Q-norm.
• Per-layer scalar gains — attn_gain and mlp_gain (initialized to (2L)^-0.5) scale the residual contributions; embed_skip (initialized to 0.0) scales an extra residual from the post-first-norm embedding into every block.
• lm_head with weight gain — stored as a raw (vocab, hidden) parameter plus a scalar lm_head_gain. Folded into a regular nn.Linear weight in sanitize() so quantization treats it normally.

Conversion details

These weights were produced by running mlx_lm.convert on lewtun/talkie-1930-13b-it-hf after adding the new talkie model module to mlx-lm. The conversion was generated and validated with the transformers-to-mlx skill.

Numerical agreement vs the upstream transformers model on a 94-token paragraph prompt (CPU, bf16 both sides):

Logits diff:    max=2.0000   mean=0.0785   median=0.0625
Top-10 overlap: 10/10  (last position)
Top-1 agreement: 98.9% (across all 94 positions)

Within typical bf16 transformers/MLX disagreement.

The 4-bit variants required architecture-aware tuning. Bare q4 produced repetition on long greedy decoding, so two recovery paths are shipped:

• -mlx-4bit — mixed-precision recipe via custom quant_predicate. A per-block sensitivity scan (in-memory mx.quantize → mx.dequantize then logit MSE vs bf16) flagged blocks 14, 37, and 38 as outliers. Final config: lm_head=q8, embed=bf16, blocks {14, 37, 38} at q8, all other Linear layers at q4.
• -mlx-4bit-DWQ — mlx_lm.dwq distillation calibration with default learning rate (1e-6, 512 samples, 512-token sequences, batch 1, gradient checkpointing). 512 iterations, final validation loss 0.037. Beats the mixed-q4 build on long-form generation.

mlx_lm.awq is not yet supported for talkie — the AWQ scaling step requires absorbing an input-scale into the upstream norm's weight, but Talkie's RMSNorms have no learned weight.

License

Apache 2.0 — same as upstream.