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Phi-3-medium-4k-instruct-4bit

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I appreciate the work that people are doing to make models available for MLX, but I've found that many of the models uploaded to mlx-community don't work. For example, this is what I get when I run this one:

from mlx_lm import load, generate

tokenizer_config = {
    'eos_token': "<|end|>"
}

model_id = "mlx-community/Phi-3-medium-4k-instruct-4bit"
user_message = "Name a color."
model, tokenizer = load(model_id, tokenizer_config=tokenizer_config)
prompt = f"<s><|user|>\n{user_message}<|end|>\n<|assistant|>\n"
response = generate(model, tokenizer, prompt=prompt, temp=0.5, max_tokens=1000, verbose=True)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[2], line 12
     10 model, tokenizer = load(model_id, tokenizer_config=tokenizer_config)
     11 prompt = f"<s><|user|>\n{user_message}<|end|>\n<|assistant|>\n"
---> 12 response = generate(model, tokenizer, prompt=prompt, temp=0.5, max_tokens=1000, verbose=True)

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/utils.py:247, in generate(model, tokenizer, prompt, temp, max_tokens, verbose, formatter, repetition_penalty, repetition_context_size, top_p, logit_bias)
    244 tic = time.perf_counter()
    245 detokenizer.reset()
--> 247 for (token, prob), n in zip(
    248     generate_step(
    249         prompt_tokens,
    250         model,
    251         temp,
    252         repetition_penalty,
    253         repetition_context_size,
    254         top_p,
    255         logit_bias,
    256     ),
    257     range(max_tokens),
    258 ):
    259     if n == 0:
    260         prompt_time = time.perf_counter() - tic

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/utils.py:195, in generate_step(prompt, model, temp, repetition_penalty, repetition_context_size, top_p, logit_bias)
    192             repetition_context = repetition_context[-repetition_context_size:]
    193     return y, prob
--> 195 y, p = _step(y)
    197 mx.async_eval(y)
    198 while True:

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/utils.py:178, in generate_step.<locals>._step(y)
    176 def _step(y):
    177     nonlocal repetition_context
--> 178     logits = model(y[None], cache=cache)
    179     logits = logits[:, -1, :]
    181     if repetition_penalty:

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/models/phi3.py:183, in Model.__call__(self, inputs, cache)
    178 def __call__(
    179     self,
    180     inputs: mx.array,
    181     cache=None,
    182 ):
--> 183     out = self.model(inputs, cache)
    184     return self.lm_head(out)

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/models/phi3.py:165, in Phi3Model.__call__(self, inputs, cache)
    162     cache = [None] * len(self.layers)
    164 for layer, c in zip(self.layers, cache):
--> 165     h = layer(h, mask, c)
    167 return self.norm(h)

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/models/phi3.py:129, in TransformerBlock.__call__(self, x, mask, cache)
    123 def __call__(
    124     self,
    125     x: mx.array,
    126     mask: Optional[mx.array] = None,
    127     cache: Optional[Tuple[mx.array, mx.array]] = None,
    128 ) -> mx.array:
--> 129     r = self.self_attn(self.input_layernorm(x), mask, cache)
    130     h = x + r
    131     r = self.mlp(self.post_attention_layernorm(h))

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/models/phi3.py:86, in Attention.__call__(self, x, mask, cache)
     84     queries = self.rope(queries, offset=cache.offset)
     85     keys = self.rope(keys, offset=cache.offset)
---> 86     keys, values = cache.update_and_fetch(keys, values)
     87 else:
     88     queries = self.rope(queries)

File ~/files/projects/python/test-env/.venv/lib/python3.12/site-packages/mlx_lm/models/base.py:34, in KVCache.update_and_fetch(self, keys, values)
     31         self.keys, self.values = new_k, new_v
     33 self.offset += keys.shape[2]
---> 34 self.keys[..., prev : self.offset, :] = keys
     35 self.values[..., prev : self.offset, :] = values
     36 return self.keys[..., : self.offset, :], self.values[..., : self.offset, :]

ValueError: Shapes (1,10,13,256) and (1,10,13,128) cannot be broadcast.